Dear galaxy users,
I aligned my RNA-seq data by using Tophat in galaxy. It generated
some
"Tophat deletions", "Tophat insertions" and "Tophat splice junctions"
results. These are all BED files. Does anyone know how to use/analyze
these
kind of results?
Also, I used illumina RNA-seq. Each biological sample has 36-48
million
reads. The data for each sample were divided to 10-12 FASTQ files.
When I
did the "FASTQ Summary Statistics" and draw "boxplot" for each of the
sub-file, the score value is about 9-10. Is it too low? Shall I
combine the
FASTQ files for each biological sample and do the statistics again?
At last, does anyone know how to convert a long list of zebrafish
genes
(500-1000 genes) to human or mammalian orthologs?
Thank you for your replies,
Xiefan Fang
University of Florida

Hello Xiefan,
Please see 'Tools on the Main server':
http://wiki.g2.bx.psu.edu/Support#Interpreting_scientific_results
The RNA-seq tutorial (hosted at Galaxy) and the web sites/paper by the
tool authors should give you many good ideas for potential protocols.
Combining the files will not change the quality values. If this is a
Phred+33 scaled quality score, then yes, this is low. A double check
that the 'FASTQ Groomer' was run with the correct options would be the
first step. You also may want to run FastQC to generate broader
statistics. See the RNA-seq tutorial for details about running this
tool
and then trimming sequences to improve overall quality.
A direct link is:
http://main.g2.bx.psu.edu/u/jeremy/p/galaxy-rna-seq-analysis-exercise
There are a likely many ways to do this, here are some:
1 - 'Get Data -> UCSC Main'
Track named "Human Proteins" with the primary table (blastHg18KG).
2 - 'Get Data -> BioMart'
Ensemble Genes 68, Danio rerio genes (Zv9). Filters -> Homologs ->
Ortholog.
Help 'Using Galaxy'
http://main.g2.bx.psu.edu/u/galaxyproject/p/using-galaxy-2012
Protocol 1 has examples of extracting data from the UCSC Table browser
and joining data - the methods can be applied to any similar data. If
you need to manipulate files, see Protocol 2, the last example is
multi-stepped and demonstrates that just about any file can be
converted
to interval format and utilized.
3 - 'MAF predictions'
'Using Galaxy' (above) Protocol 5 has an alternate method for
predicting
"orthologs" (or maybe better described as 'syntenically conserved
homologs', since function is not evaluated) from conservation tracks.
Full details of MAF functions are in our 'Making whole genome
alignments
usable for biologists' paper:
http://main.g2.bx.psu.edu/u/dan/p/maf
The ZF Conservation track is not local to Galaxy, so you will need to
obtain the data from UCSC and FTP to Galaxy to work with it ('Get
Data'
is not an option). Review the track description in the UCSC browser
(track named "Conservation"), then find the data here:
http://hgdownload.soe.ucsc.edu/goldenPath/danRer7/multiz8way/
Good luck for the choices you decide on!
Jen
Galaxy team
--
Jennifer Jackson
http://galaxyproject.org